UNASSIGNED: Spinal Infection (SI) is associated with various comorbidities. The interaction of these comorbidities and their impact on costs and complexity of care has not been fully assessed.
UNASSIGNED: This is a retrospective cohort study of SI patients in an urban hospital system to characterize comorbidities and outcomes in adult patients with SI. Adult patients in our hospital system who were hospitalized with an initial diagnosis of SI between July 1, 2017 and June 30, 2019 were included. Outcomes measures included length of stay (LOS) of the index hospitalization for SI, charges and payments for the index hospitalization, and hospital readmissions within one year after discharge from the index hospitalization. Data was obtained by querying our Electronic Data Warehouse (EDW) using ICD-10-CM and CPT procedure codes. Spearman\'s correlation was used to summarize the relationships between LOS, charges, and payments. Multivariable linear regression was used to evaluate associations of demographics, comorbidities, and other factors with LOS. Multivariable Cox regression was used to evaluate associations of demographics, comorbidities, and other factors with hospital readmissions.
UNASSIGNED: 403 patients with a first diagnosis of SI were identified. The average number of comorbidities per patient was 1.3. 294 (73%) had at least 1 medical comorbidity, and 54 (13%) had 3 or more comorbidities. The most common medical comorbidities were diabetes mellitus (26%), intravenous drug use (IVDU, 26%), and malnutrition (20%). 112 patients (28%) had a surgical site infection (SSI). DM (p<.001) and SSI (p=.016) were more common among older patients while IVDU was more common among younger patients (p<.001). Median LOS was 12 days. A larger number of medical comorbidities was associated with a longer LOS (p<.001) while the presence of a SSI was associated with a shorter LOS (p=.007) after multivariable adjustment. LOS was positively correlated with both charges (r=0.83) and payments (r=0.61). Among 389 patients discharged after the index hospitalization, 36% had a readmission within 1 year. The rate of readmission was twice as high for patients with three or more comorbidities than patients with zero comorbidities (hazard ratio: 1.95, p=.017).
UNASSIGNED: Patients with SI often have multiple comorbidities, and the specific type of comorbidity is associated with the patient\'s age. The presence of multiple comorbidities correlates with initial LOS, cost of care, and readmission rate. Readmission in the first year post-discharge is high.

UNASSIGNED: Patients on dialysis are twice as likely to have early readmissions. This study aimed to identify risk factors for 30-day unplanned readmission among patients on maintenance dialysis in a tertiary hospital.
UNASSIGNED: We conducted a retrospective, unmatched, case-control study. Data were taken from patients on maintenance hemodialysis admitted in the University of the Philippines-Philippine General Hospital (UP-PGH) between January 2018 and December 2020. Patients with 30-day readmission were included as cases and patients with >30-day readmissions were taken as controls. Multivariable regression with 30-day readmission as the outcome was used to identify significant predictors of early readmission.
UNASSIGNED: The prevalence of 30-day unplanned readmission among patients on dialysis is 36.96%, 95%CI [31.67, 42.48]. In total, 119 cases and 203 controls were analyzed. Two factors were significantly associated with early readmission: the presence of chronic glomerulonephritis [OR 2.35, 95% CI 1.36 to 4.07, p-value=0.002] and number of comorbidities [OR 1.34, 95% CI 1.12 to 1.61, p-value=0.002]. The most common reasons for early readmission are infection, anemia, and uremia/underdialysis.
UNASSIGNED: Patients with chronic glomerulonephritis and multiple comorbidities have significantly increased odds of early readmission. Careful discharge planning and close follow up of these patients may reduce early readmissions.

OBJECTIVE: Alcohol use disorder (AUD) is a persistent public health concern, contributing significantly to mortality and morbidity. This study aims to evaluate the impact of in-hospital extended-release naltrexone (XR-NTX) administration on alcohol-related outcomes.
METHODS: This retrospective cohort study, conducted at an academic medical centre, included 141 adult patients with AUD who received XR-NTX between December 2020 and June 2021. Primary and secondary outcomes were assessed 90 days before and after XR-NTX administration to identify number of alcohol-related hospitalisations, emergency department (ED) visits and average length of hospital stay. Subgroup analyses assessed outcomes in high hospital utilisers and marginally housed or unhoused populations.
RESULTS: There was a significant decrease in ED visits and length of hospital stay post XR-NTX and no significant difference in the number of rehospitalisations. Subgroup analysis showed significant reduction in hospital readmissions and ED visits among high hospital utilisers. Our sample was a predominantly middle-aged, male and white patient population.
CONCLUSIONS: In-hospital initiation of XR-NTX for AUD was associated with a significant decrease in ED visits and length of hospital stay. While no significant impact on the number of hospitalisations was observed overall, there was a substantial reduction in hospital readmissions and ED visits among high utilisers. Our findings suggest the potential benefits of in-hospital XR-NTX, emphasising the need for further research to establish causal relationships, assess cost-effectiveness and explore effectiveness across diverse patient populations. Effective in-hospital interventions, such as XR-NTX, hold promise for improving patient outcomes and reducing the healthcare burden associated with AUD.

BACKGROUND: Little is known about the prevalence of malnutrition among patients receiving home care (HC) and ambulatory care (AC) services. Further, the risk of hospital readmission in malnourished patients transitioning from hospital to HC or AC is also not well established. This study aims to address these two gaps.
METHODS: A descriptive cohort study of newly referred HC and AC patients between January and December 2019 was conducted. Nutrition status was assessed by clinicians using the Mini Nutritional Assessment-Short Form (MNA-SF). Prevalence of malnutrition and at risk of malnutrition (ARM) was calculated, and a log-binomial regression model was used to estimate the relative risk of hospital readmission within 30 days of discharge for those who were malnourished and referred from hospital.
RESULTS: A total of 3704 MNA-SFs were returned, of which 2402 (65%) had complete data. The estimated prevalence of malnutrition and ARM among newly referred HC and AC patients was 21% (95% CI: 19%-22%) and 55% (95% CI: 53%-57%), respectively. The estimated risk of hospital readmission for malnourished patients was 2.7 times higher (95% CI: 1.9%-3.9%) and for ARM patients was 1.9 times higher (95% CI: 1.4%-2.8%) than that of patients with normal nutrition status.
CONCLUSIONS: The prevalence of malnutrition and ARM among HC and AC patients is high. Malnutrition and ARM are correlated with an increased risk of hospital readmission 30 days posthospital discharge.

OBJECTIVE: (1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index.
METHODS: We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program.
METHODS: We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans\' health-related social risk.
METHODS: We included 156,690 Veterans\' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022.
RESULTS: The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA.
CONCLUSIONS: Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.

BACKGROUND: Diabetic foot (DF) is part of the natural history of diabetes mellitus, ulceration being a severe complication with a prevalence of approximately 6.3 %, which confers a significant economic burden. Hospital readmission in the first thirty (30) days is considered a measure of quality of healthcare and it\'s been identified that the most preventable causes are the ones that occur in this period. This study seeks to identify the risk factors associated with readmission of patients with DF.
METHODS: A case-control study was done by performing a secondary analysis of a database. Descriptive statistics were used for all variables of interest, bivariate analysis to identify statistically significant variables, and a logistic regression model for multivariate analysis.
RESULTS: 575 cases were analyzed (113 cases, 462 controls). A 20 % incidence rate of 30-day readmission was identified. Statistically significant differences were found in relation to the institution of attention (Hospital Universitario de la Samaritana: OR 1.9, p value < 0.01, 95 % CI 1.2-3.0; Hospital Universitario San Ignacio: OR 0.5, p value < 0.01, 95 % CI 0.3-0.8) and the reasons for readmission before 30 days, especially due to surgical site infection (SSI) (OR 7.1, p value < 0.01, 95 % CI 4.1-12.4), sepsis (OR 8.4, p value 0.02, 95 % CI 1.2-94.0), dehiscence in amputation stump (OR 16.4, p value < 0.01, 95 % CI 4.2-93.1) and decompensation of other pathologies (OR 3.5, p value < 0.01, 95 % CI 2.1-5.7).
CONCLUSIONS: The hospital readmission rate before 30 days for our population compares to current literature. Our results were consistent with exacerbation of chronic pathologies, but other relevant variables not mentioned in other studies were the hospital in which patients were taken care of, the presence of SSI, sepsis, and dehiscence of the amputation stump. We consider thoughtful and close screening of patients at risk in an outpatient setting might identify possible readmissions.

OBJECTIVE: We explore patient-reported behaviors and activities within 30-days post-stroke hospitalization and their role in reducing death or readmissions within 90-days post-stroke.
METHODS: We constructed the adequate transitions of care (ATOC) composite score, measuring patient-reported participation in eligible behaviors and activities (diet modification, weekly exercise, follow-up medical appointment attendance, medication adherence, therapy use, and toxic habit cessation) within 30 days post-stroke hospital discharge. We analyzed ATOC scores in ischemic and intracerebral hemorrhage stroke patients discharged from the hospital to home or rehabilitation facilities and enrolled in the NIH-funded Transitions of Care Stroke Disparities Study (TCSD-S). We utilized Cox regression analysis, with the progressive adjustment for sociodemographic variables, social determinants of health, and stroke risk factors, to determine the associations between ATOC score within 30-days and death or readmission within 90-days post-stroke.
RESULTS: In our sample of 1239 stroke patients (mean age 64 +/- 14, 58 % male, 22 % Hispanic, 22 % Black, 52 % White, 76 % discharged home), 13 % experienced a readmission or death within 90 days (3 deaths, 160 readmissions, 3 readmissions with subsequent death). Seventy percent of participants accomplished a ≥75 % ATOC score. A 25 % increase in ATOC was associated with a respective 20 % (95 % CI 3-33 %) reduced risk of death or readmission within 90-days.
CONCLUSIONS: ATOC represents modifiable behaviors and activities within 30-days post-stroke that are associated with reduced risk of death or readmission within 90-days post-stroke. The ATOC score should be validated in other populations, but it can serve as a tool for improving transitions of stroke care initiatives and interventions.

BACKGROUND: Patients hospitalized with COVID-19 can clinically deteriorate after a period of initial stability, making optimal timing of discharge a clinical and operational challenge.
OBJECTIVE: To determine risks for post-discharge readmission and death among patients hospitalized with COVID-19.
METHODS: Multicenter retrospective observational cohort study, 2020-2021, with 30-day follow-up.
METHODS: Adults admitted for care of COVID-19 respiratory disease between March 2, 2020, and February 11, 2021, to one of 180 US hospitals affiliated with the HCA Healthcare system.
METHODS: Readmission to or death at an HCA hospital within 30 days of discharge was assessed. The area under the receiver operating characteristic curve (AUC) was calculated using an internal validation set (33% of the HCA cohort), and external validation was performed using similar data from six academic centers associated with a hospital medicine research network (HOMERuN).
RESULTS: The final HCA cohort included 62,195 patients (mean age 61.9 years, 51.9% male), of whom 4704 (7.6%) were readmitted or died within 30 days of discharge. Independent risk factors for death or readmission included fever within 72 h of discharge; tachypnea, tachycardia, or lack of improvement in oxygen requirement in the last 24 h; lymphopenia or thrombocytopenia at the time of discharge; being ≤ 7 days since first positive test for SARS-CoV-2; HOSPITAL readmission risk score ≥ 5; and several comorbidities. Inpatient treatment with remdesivir or anticoagulation were associated with lower odds. The model\'s AUC for the internal validation set was 0.73 (95% CI 0.71-0.74) and 0.66 (95% CI 0.64 to 0.67) for the external validation set.
CONCLUSIONS: This large retrospective study identified several factors associated with post-discharge readmission or death in models which performed with good discrimination. Patients 7 or fewer days since test positivity and who demonstrate potentially reversible risk factors may benefit from delaying discharge until those risk factors resolve.

OBJECTIVE: Despite increased availability of methodologies to identify algorithmic bias, the operationalization of bias evaluation for healthcare predictive models is still limited. Therefore, this study proposes a process for bias evaluation through an empirical assessment of common hospital readmission models. The process includes selecting bias measures, interpretation, determining disparity impact and potential mitigations.
METHODS: This retrospective analysis evaluated racial bias of four common models predicting 30-day unplanned readmission (i.e., LACE Index, HOSPITAL Score, and the CMS readmission measure applied as is and retrained). The models were assessed using 2.4 million adult inpatient discharges in Maryland from 2016 to 2019. Fairness metrics that are model-agnostic, easy to compute, and interpretable were implemented and apprised to select the most appropriate bias measures. The impact of changing model\'s risk thresholds on these measures was further assessed to guide the selection of optimal thresholds to control and mitigate bias.
RESULTS: Four bias measures were selected for the predictive task: zero-one-loss difference, false negative rate (FNR) parity, false positive rate (FPR) parity, and generalized entropy index. Based on these measures, the HOSPITAL score and the retrained CMS measure demonstrated the lowest racial bias. White patients showed a higher FNR while Black patients resulted in a higher FPR and zero-one-loss. As the models\' risk threshold changed, trade-offs between models\' fairness and overall performance were observed, and the assessment showed all models\' default thresholds were reasonable for balancing accuracy and bias.
CONCLUSIONS: This study proposes an Applied Framework to Assess Fairness of Predictive Models (AFAFPM) and demonstrates the process using 30-day hospital readmission model as the example. It suggests the feasibility of applying algorithmic bias assessment to determine optimized risk thresholds so that predictive models can be used more equitably and accurately. It is evident that a combination of qualitative and quantitative methods and a multidisciplinary team are necessary to identify, understand and respond to algorithm bias in real-world healthcare settings. Users should also apply multiple bias measures to ensure a more comprehensive, tailored, and balanced view. The results of bias measures, however, must be interpreted with caution and consider the larger operational, clinical, and policy context.

UNASSIGNED: The coronavirus pandemic highlighted the need for remote patient monitoring to deliver and provide access to patient care and education. A mobile-based app providing interactive tools for patient education and monitoring was piloted at Thunder Bay Regional Health Sciences Centre (TBRHSC) in November 2020. We aimed to examine the platform\'s impact on postoperative length of stay, hospital readmissions, and emergency department (ED) visits 60 days postsurgery in total hip and knee arthroplasty patients in Northwestern Ontario.
UNASSIGNED: Data were assessed from patients undergoing primary total hip or knee arthroplasties at TBRHSC from March 1, 2020, to February 28, 2022. Patients were divided into 2 cohorts based on enrollment with the mobile-based app (SeamlessMD). Statistical differences in outcomes were determined using Mann-Whitney or χ2 tests. An odds ratio was calculated for ED visits.
UNASSIGNED: Patients enrolled in the mobile-based app had statistically lower length of stay (U = 7779.0, P < .001) and fewer ED visits (χ2 (1,212) = 5.570, P = .018) than patients not enrolled in the program. Patients not enrolled had 2.31 times greater odds of visiting the ED postsurgery (odds ratio = 0.432, 95% confidence interval = 0.213-0.877, P = .022). There were no statistical differences found in readmission rates.
UNASSIGNED: The implementation of the mobile-based app at TBRHSC showed its potential value as a tool to reduce costs in the healthcare system and improve patient outcomes. Consequentially, more formal studies are required to elucidate the magnitude of this effect.